System, method and computer program product for object examination

ABSTRACT

Inspection data that corresponds to potential defects of an object may be received. A first set of locations of first potential defects can be identified. The first set of locations of the first potential defects can be imaged with a review tool to obtain a first set of review images. The first potential defects can be classified based on the first set of review images to obtain first classification results of the first potential defects. An instruction can be determined for the review tool based on the first classification results, the instruction being associated with detecting potential defects. Using the instruction, a second set of locations of second potential defects of the plurality of potential defects to be imaged with the review tool can be identified.

RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/553,946, filed on Aug. 28, 2019, to be issued onDec. 22, 2020 as U.S. Pat. No. 10,871,451, which is a continuation ofU.S. patent application Ser. No. 15/703,937, filed on Sep. 13, 2017 andissued on Sep. 10, 2019 as U.S. Pat. No. 10,408,764, all and each whichare hereby incorporated by reference.

TECHNICAL FIELD

The presently disclosed subject matter relates to examining objects(e.g. wafers, reticles, etc.) and more particularly to detecting defectsin objects by examining captured images of the objects.

BACKGROUND

Current demands for high density and performance, associated with ultralarge scale integration of fabricated devices, require submicronfeatures, increased transistor and circuit speeds, and improvedreliability. Such demands require formation of device features with highprecision and uniformity, which, in turn, necessitate careful monitoringof the fabrication process, including frequent and detailed inspectionof the devices while they are still in the form of semiconductor wafers.

The terms “specimen” or “object” used in this specification should beexpansively construed to cover any kind of wafer, masks, and otherstructures, combinations and/or parts thereof used for manufacturingsemiconductor integrated circuits, magnetic heads, flat panel displays,and other semiconductor-fabricated articles.

The term “defect” used in this specification should be expansivelyconstrued to cover any kind of abnormality or undesirable feature formedon or within a wafer.

The complex manufacturing process of objects is not error-free and sucherrors may cause faults in the manufactured objects. The faults mayinclude defects that can harm operation of the object, and nuisances,which may be defects, but do not cause any harm or malfunction of themanufactured unit. By way of non-limiting examples, defects may becaused during the manufacturing process, due to faults in the rawmaterial; mechanical, electrical or optical errors; human errors orothers. Further defects may be caused by spatio-temporal factors, suchas temperature changes of the wafer occurring after one or moremanufacturing stages during the examination process, which may causesome deformations of the wafer. The examination process can alsointroduce further alleged errors, for example due to optical, mechanicalor electrical problems in the examination equipment or process, whichthus provide imperfect captures. Such errors may produce false positivefindings, which may seem to contain a defect, but no actual defectexists at the area.

In many applications, the type, or class, of a defect is of importance.For example, defects may be classified into one of a number of classes,such as a particle, a scratch, process, or the like.

Unless specifically stated otherwise, the term “image” used in thespecification should be expansively construed to cover anynon-destructive capturing of an object, including, but not limited to,capturing by an optical device, capturing by a scanning electronmicroscope, or by any other suitable device or tool.

Unless specifically stated otherwise, the term “examination” used inthis specification should be expansively construed to cover any kind ofdetection and/or classification of defects in an object. Examination isprovided by using non-destructive examination tools during or aftermanufacture of the object to be examined. By way of non-limitingexample, the examination process can include scanning (in a single or inmultiple scans), sampling, reviewing, measuring, classifying and/orother operations provided with regard to the object or parts thereof,using one or more examination tools. Likewise, examination can beprovided prior to manufacture of the object to be examined and caninclude, for example, generating an examination recipe(s). It is notedthat, unless specifically stated otherwise, the term “examination” orits derivatives used in this specification are not limited with respectto the size of the inspected area(s), to the speed or resolution of thescanning or to the type of examination tools. A variety ofnon-destructive examination tools includes, by way of non-limitingexample, optical tools, scanning electron microscopes, atomic forcemicroscopes, etc.

The examination process can include a plurality of examination steps.During the manufacturing process, the examination steps can be performeda multiplicity of times, for example after the manufacturing orprocessing of certain layers, or the like. Additionally oralternatively, each examination step can be repeated multiple times, forexample for different wafer locations or for the same wafer locationswith different examination settings.

By way of non-limiting example, run-time examination can employ atwo-step procedure, e.g. inspection of a specimen followed by review ofsampled defects. During the inspection step, the surface of a specimenor a part thereof (e.g. areas of interest, hot spots, etc.) is typicallyscanned at relatively high-speed and/or low-resolution. The capturedinspection image is analyzed in order to detect defects and obtainlocations and other inspection attributes thereof. At the review stepthe images of at least part of defects detected during the inspectionphase are, typically, captured at relatively low speed and/orhigh-resolution, thereby enabling classification and, optionally, otheranalyses of the at least part of defects. In some cases both phases canbe implemented by the same inspection tool, and, in some other cases,these two phases are implemented by different inspection tools.

SUMMARY

One aspect of the disclosed subject matter relates to an examinationsystem comprising: a defect detection system comprising a processing andmemory circuitry (PMC) and configured to receive inspection datacomprising at least one inspection image informative of potentialdefects of an object; and a review tool configured to review at leastpart of the potential defects. PMC is configured to: upon accommodationin a memory the received inspection data, process the at least one imageusing a first recipe to detect a first set of locations of firstpotential defects and attributes thereof; select at least part of thefirst set of locations and image the selected at least part of the firstset of locations with a review tool to obtain a first set of reviewimages; based on the first set of review images, obtain firstclassification results informative of classification of at least part ofthe first potential defects corresponding to the selected at least partof the first set of locations; determine a further recipe using thefirst classification results; process the at least one image using thefurther recipe to detect a further set of locations of further potentialdefects and attributes thereof; select at least part of the further setof locations and image the selected at least part of the further set oflocations with a review tool to obtain a further set of review images;based on the further set of review images, obtain further classificationresults informative of classification of at least part of the furtherpotential defects corresponding to the selected at least part of thefurther set of locations; and repeat determining a next further recipe,processing the at least one image to detect a next further set oflocations of a next further potential defects and attributes thereof,selecting at least part of the next further set of locations, imagingthe at least part of the next further set of locations, and obtainingnext further classification results, until an examination stoppingcriteria is met.

Within the examination system, detecting the further set of locationsoptionally comprises: segmenting the one or more of the images intosegments in accordance with noise levels within each of the segments;determining a grade for elements within the segments, the gradeindicative of a chance of each element to contain a defect; anddetecting the further potential defects from elements of the segments inaccordance with a threshold. Within the examination system, theinspection tool and the review tool are optionally one examination tooloperated at different modes. Within the examination system, the PMB isoptionally a part of the inspection tool. Within the examination system,the PMB is optionally a part of the review tool. Within the examinationsystem, the PMB is optionally separate from the inspection tool and fromthe review tool.

Another aspect of the disclosed subject matter relates to a method ofexamining an object. The method comprises: processing, by the processor,using a first recipe at least one image to detect a first set oflocations of first potential defects and attributes thereof, the atleast one image comprised in inspection data generated by an inspectiontool and stored in the memory; selecting, by the processor, at leastpart of the first set of locations and imaging by a review tool theselected at least part of the first set of locations with a review toolto obtain a first set of review images; based on the first set of reviewimages, obtaining, by the processor, first classification resultsinformative of classification of at least part of the first potentialdefects corresponding to the selected at least part of the first set oflocations; determining, by the processor, a further recipe using thefirst classification results; processing, by the processor, the at leastone image using the further recipe to detect a further set of locationsof further potential defects and attributes thereof; selecting, by theprocessor, at least part of the further set of locations and imaging bythe review tool the selected at least part of the further set oflocations with a review tool to obtain a further set of review images;based on the further set of review images, obtaining, by the processor,further classification results informative of classification of at leastpart of the further potential defects corresponding to the selected atleast part of the further set of locations; and repeating, by theprocessor, determining a next further recipe, processing the at leastone image to detect a next further set of locations of a next furtherpotential defects and attributes thereof, selecting at least part of thenext further set of locations, imaging the at least part of the nextfurther set of locations, and obtaining next further classificationresults, until an examination stopping criteria is met.

Within the method, one or more elements are optionally selected from thegroup consisting of: a pixel; a group of pixels; a feature and ageometrical area. Within the method, the further set of locationsoptionally does not include a location from a previous set of locations.The method can further comprise repeating said sampling the part of thefurther set of locations and obtaining further classification resultsper each further potential defect obtained, until a selection stoppingcriteria is met. Within the method, the selection stopping criteria isoptionally selected from the group consisting of: all further set oflocations have been reviewed, or a representative selection from thefurther set of locations has been reviewed. Within the method, theexamination stopping criteria is optionally selected from the groupconsisting of: time allotted for examination is over; a number of reviewoperations has reached a threshold; a number of true defects determinedhas reached a predetermined threshold; and a number of true defectsdetermined on a previous one or more collections has decreased below apredetermined threshold. Within the method, classification is optionallyinto a true defect or a non-defect. Within the method, classification isoptionally into a severe defect, a non-severe defect, a nuisance, or afalse alarm. Within the method, obtaining the further recipe optionallyuses classification results obtained with any previous recipe.

Yet another aspect of the disclosed subject matter relates to anon-transitory computer readable medium comprising instructions that,when executed by a computer, cause the computer to perform a method ofexamination of a semiconductor specimen as above.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the invention and to see how it may be carriedout in practice, embodiments will now be described, by way ofnon-limiting example only, with reference to the accompanying drawings,in which:

FIG. 1 illustrates a block diagram of an examination system, inaccordance with certain embodiments of the presently disclosed subjectmatter;

FIG. 2A illustrates a generalized model of a prior art process of objectexamination;

FIG. 2B illustrates a generalized model of a process of objectexamination, in accordance with certain embodiments of the presentlydisclosed subject matter;

FIG. 3 illustrates a generalized flow-chart of an object examinationprocess, in accordance with certain embodiments of the presentlydisclosed subject matter; and

FIG. 4 shows an illustrative example of defect detection withsegmentation, in accordance with certain embodiments of the presentlydisclosed subject matter.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresently disclosed subject matter may be practiced without thesespecific details. In other instances, well-known methods, procedures,components and circuits have not been described in detail so as not toobscure the presently disclosed subject matter.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “determining”, “calculating”,“processing”, “computing”, “representing”, “comparing”, “generating”,“assessing”, “matching”, “processing”, “selecting”, “detecting”,“sampling”, “assigning” or the like, refer to the action(s) and/orprocess(es) of a computer that manipulate and/or transform data intoother data, said data represented as physical, such as electronic,quantities and/or said data representing the physical objects. The term“computer” should be expansively construed to cover any kind ofhardware-based electronic device with data processing capabilitiesincluding, by way of non-limiting example, an ADI system and partsthereof disclosed in the present application.

The terms “non-transitory memory” and “non-transitory storage medium”used herein should be expansively construed to cover any volatile ornon-volatile computer memory suitable to the presently disclosed subjectmatter.

The term “recipe” refers to a set of parameters used by an imagingdevice such as an inspection device for capturing an object andanalyzing the captured images. The recipe can include capture-relatedattributes such as light projecting conditions, light collectionconditions, machine configuration, or others, and analysis-relatedparameters, such as noise level, thresholds for indicating a location asa potential defect, segmentation parameters, or others.

It is appreciated that, unless specifically stated otherwise, certainfeatures of the presently disclosed subject matter, which are describedin the context of separate embodiments, can also be provided incombination in a single embodiment. Conversely, various features of thepresently disclosed subject matter, which are described in the contextof a single embodiment, can also be provided separately or in anysuitable sub-combination. In the following detailed description,numerous specific details are set forth in order to provide a thoroughunderstanding of the methods and apparatus.

Bearing this in mind, attention is drawn to FIG. 1 illustrating a blockdiagram of an examination system in accordance with certain embodimentsof the presently disclosed subject matter. Examination system 100illustrated in FIG. 1 can be used for examination of an object (e.g. awafer and/or parts thereof) for defects as a part of object fabrication.The examination can be part of the object fabrication and can be carriedout during manufacturing of the object, or afterwards. The illustratedexamination system 100 comprises computer-based defect detection system103 capable of automatically determining defect-related informationusing images obtained during or after object fabrication (referred tohereinafter as images), and/or derivatives thereof. Defect detectionsystem 103 is referred to hereinafter as FPEI (Fabrication ProcessExamination Information) system 103. FPEI system 103 can be operativelyconnected to one or more inspection examination tools 101 and/or one ormore review tools 102. Inspection tools 101 are configured to captureinspection images (typically, at relatively high-speed and/orlow-resolution). Review tools 102 are configured to capture reviewimages of at least part of defects detected by inspection tools 101(typically, at relatively low-speed and/or high-resolution).

FPEI system 103 can be further operatively connected to design server110 comprising design data of the object, such as Computer Aided Design(CAD) data.

An object can be examined by an inspection tool 101 (e.g. an opticalinspection system, low-resolution SEM, etc.). The resulting imagesand/or derivatives thereof informative of revealed potential defects(collectively referred to hereinafter as inspection data 121) can betransmitted—directly or via one or more intermediate systems—to FPEIsystem 103. As will be further detailed with reference to the figuresbelow, FPEI system 103 is configured to receive, via input interface105, data produced by inspection tool 101 and/or data stored in designserver 110 and/or another relevant data depository. Inspection data 121,including images and/or additional data or metadata can be stored in andretrieved from storage 112.

FPEI system 103 is further configured to process the received data andsend, via output interface 106, the results (or part thereof) to astorage system, to examination tool(s), to a computer-based graphicaluser interface (GUI) 120 for rendering the results and/or to externalsystems (e.g. Yield Management System (YMS) of a FAB, recipe node,etc.). GUI 120 can be further configured to enable user-specified inputsrelated to operating FPEI system 103.

As will be further detailed with reference to the figures below, FPEIsystem 103 can be configured to process the received inspection data(optionally together with other data as, for example, design data and/ordefect classification data) to select potential defects for review. Itis noted that the potential defects for review are referred tohereinafter also as defects for review.

FPEI system 103 can send the processing results (e.g.instruction-related data) to any of the examination tool(s), store theresults (e.g. defect classification) in a storage system, render theresults via GUI 230 and/or send to an external system (e.g. to YMS,recipe node, etc.).

The specimen can be further examined by review tool 102. A subset ofpotential defect locations selected for review in accordance with datagenerated by FPEI system 103 can be reviewed by a scanning electronmicroscope (SEM) or Atomic Force Microscopy (AFM), etc. The resultingdata informative of review images and/or derivatives thereof can betransmitted—directly or via one or more intermediate systems

-   -   to FPEI system 103 and can be used for further selection of        potential defects for review, classifying the reviewed defects,        etc.

FPEI system 103 comprises a processor and memory circuitry (PMC) 104operatively connected to a hardware-based input interface 105 and to ahardware-based output interface 106. PMC 104 is configured to provideprocessing necessary for operating FPEI system 103 as further detailedwith reference to the following figures and comprises a processor (notshown separately) and a memory (not shown separately). The processor ofPMC 104 can be configured to execute several functional modules inaccordance with computer-readable instructions implemented on anon-transitory computer-readable memory comprised in PMC 104. Suchfunctional modules are referred to hereinafter as comprised in PMC 104.Functional modules comprised in PMC 104 can include defectsdetermination module 108, selection module 136 and recipe determinationmodule 140 Defects determination module 108 can include segmentationmodule 124, grading module 128 and detection module 132. Operating ofPMC 104 and functional modules therein is further detailed withreference to FIG. 3 .

It will be appreciated that inspection tool 101 and review tool 102 canbe different tools located at the same or at different locations, or asingle tool operated in two different modes. In the latter case, thetool may be first operated with lower resolution and high speed toobtain images of all or at least a large part of the relevant areas ofthe object. Once potential defects are detected, the tool can beoperated at a higher resolution and possibly lower speed for examiningspecific locations associated with the potential defects.

Those versed in the art will readily appreciate that the teachings ofthe presently disclosed subject matter are not bound by the systemillustrated in FIG. 1 , and that equivalent and/or modifiedfunctionality can be consolidated or divided in another manner and canbe implemented in any appropriate combination of software with firmwareand hardware.

It is noted that FPEI system 103 illustrated in FIG. 1 can beimplemented in a distributed computing environment, in which theaforementioned functional modules shown in FIG. 1 can be distributedover several local and/or remote devices, and can be linked through acommunication network. It is further noted that in other embodiments atleast some of examination tools 101 and/or 102, storage 112 and/or GUI120 can be external to examination system 100 and operate in datacommunication with FPEI system 103, for example via input interface 105and output interface 106. FPEI system 103 can be implemented asstand-alone computer(s) to be used in conjunction with the examinationtools. Alternatively, the respective functions of FPEI system 103 canbe, at least partly, integrated with one or more examination tools,process control tools, recipe generation tools, systems for automaticdefects review and/or classification, and/or other systems related toexamination. Unless explicitly indicated otherwise, the descriptionbelow uses the terms “potential defects”, “locations of potentialdefects” and “locations” interchangeably, since each defect may beidentified by its location, each location may be a potential defect, andeach defect may be verified by examining the respective location andpossibly its surrounding area with a review tool.

Reference is now made to FIG. 2A, illustrating a generalized model of aprior art process of object examination.

The examination process starts by inspection tool 101 imaging (200) anobject and capturing one or more images of an object to be examined. Theimages may cover the whole area of one or more layers in the object, orany part thereof. The images are taken and analyzed using a defaultrecipe, or a recipe revised, for example, as a result of capturing asample object of the type of the examined object. The recipe mayindicate parameters such as one or more light conditions for capturingimages.

An examination system analyzes the captured images and detects (204) amultiplicity of potential defects' locations. The analysis may also useadditional parameters of the predetermined recipe for determining thepotential defects, for example by using predetermined thresholds,preferring potential defects appearing in images taken under specificlight conditions, or the like.

The examination system can then select (208) from the detected defects amultiplicity of locations for review. The locations can be selected ascorresponding to potential defects having a highest probability to betrue defects. Additionally or alternatively, the defects can be selectedin a uniform distribution over the object area, or in accordance withother considerations.

The selected locations can then be imaged (212) by review tool 102, fromwhich they can be classified. In some exemplary embodiments, theclassification can be into a true defect or a false alarm. In otherexemplary embodiments, further classes may be defined into which apotential defect can be classified, such as a defect, a severe defect, anuisance, or a false alarm. Further classification can be into defecttypes, such as defects associated with a particular design feature,defects associated with layer mismatch, or the like.

The obtained results are output (216) to a user, a file, another system,or the like.

Based on the results of the imaging by review tool 102, furtherpotential defects can be selected (208) from the potential defects asdetected for review at 204.

The sampled potential defects are then imaged (212) using review 102.

Selecting 208 and imaging 212 can be repeated in a loop until one ormore predetermined examination criteria are met, such as a maximalnumber of repetitions, a maximal number of true defects identified, amaximal number of review operations performed, a percentage of new truedefects found in a predetermined number of repetitions being below apredetermined threshold, or others.

It is appreciated that in the flow of FIG. 2A, selection 208 is limitedto the potential defects as detected on 204, such that the detection isnot repeated, and potential defects that have not been detected from theinspection results will not be detected at a later time.

Reference is now made to FIG. 2B, illustrating a generalized model of aprocess of object examination, in accordance with certain embodiments ofthe presently disclosed subject matter.

The examination process starts by inspection tool 101 obtaining (200)one or more images of an object to be examined. The images may cover thewhole area of one or more layers in the object, or any part thereof. Theimages are taken using a default recipe, or a recipe determined forexample when capturing a sample object of the kind of the examinedobject.

The images, optionally with additional information such as attributes ofthe images or of specific locations therein, are stored in image storage112, such as but not limited to a Network-attached storage (NAS) orsolid-state drives (SSD), which is accessible to FPEI system 103.

It will be appreciated that while images of a first object are beingstored within image storage 112, or processed by FPEI system 103,another object may already be imaged by inspection tool 101, thusincreasing system throughput.

FPEI system 103 can then detect (228) defects from the inspection imagesas retrieved from image storage 112.

FPEI system 103 can then select (210) a multiplicity of defects'locations from the detected potential defects locations, to be examinedby review tool 102.

The selected potential defects are then imaged (212) using review tool102, and can then be classified.

The obtained classification results can be output (216) to a user, afile, another system, or the like.

The results can also be provided back to FPEI system 103, and a furthermultiplicity of potential defect locations can be selected (210) fromthe detected potential defects, imaged (212) by the inspection tool 102and classified.

The selection 210 and imaging 212 can be repeated until a selectionstopping criteria is met, meaning that the multiplicity of potentialdefects detected on 228 is exhausted, for example has been fullyreviewed, the number of true defects identified by additional eachrepetition is below a predetermined threshold, or the like.

FPEI system 103 can then determine a new or updated recipe based on theclassification results, and can use the new or updated recipe to detect(228) potential defects to be imaged by review tool 102. The detectionis not limited to any potential defects previously detected for review.Rather, any location depicted in any of the images taken by inspectiontool 101 and stored in image storage 112 can be detected, thus providingfor a more efficient defect detection process, since the locations to bereviewed are chosen based on knowledge accumulated iteratively and arenot limited to an initial list compiled under less information.

Thus, execution can return to detecting (228) potential defects from theinspection images as stored and not from a predetermined collection. Thedetected defects can but do not have to include additional potentialdefects not selected on previous selection steps.

FPEI system 103 can then select (210) and examine (212) locations fromthe currently selected potential defects with review tool 102. Selection210 and examination 212 by review tool 102 can then repeat until theselection stopping criteria is met for the current detected defects.

The detection, followed by the selection, and review which may berepeated for each detection, can repeat until one or more examinationstopping criteria is met, such as a maximal number of selectionrepetitions, a minimal or maximal number of true defects identified, amaximal number of review operations performed, a percentage of new truedefects found in a predetermined number of repetitions being below apredetermined threshold, or the like.

Reference is now made to FIG. 3 , showing a generalized flowchart of aprocess of object examination, in accordance with certain embodiments ofthe presently disclosed subject matter.

FPEI system 103 can obtain (300), for example receive over acommunication channel, read from a file, or the like, output ofinspection of the object, including the images as captured and possiblyadditional data such as scanning parameters, meta data or the like. Thecaptured images of the object may be stored, optionally together withthe additional data, such as the scanning parameters, in image storage112, to form an image data set.

FPEI system 103 can detect (304) potential defects from the output ofinspection tool 101, using for example a recipe which may also have beenused for capturing the images by inspection tool 101. The potentialdefects can be detected based on considerations such as providinguniform coverage to all areas, providing extra coverage to areas knownto be problematic, using thresholds in accordance with the design dataor the specific object, or the like.

Once potential defects are detected, FPEI system 103 can select (316)part of the potential defects for imaging by review tool 102. Selection316 can be in accordance with considerations such as exploration vs.exploitation, region of interest (ROI), i.e. preferred areas, defectsignature, or the like.

Review tool 102 can image (320) the locations of all or part of thepotential defects.

Review tool 102 or FPEI system 103 can classify (322) the potentialdefects into the classes in accordance with the obtained images, toobtain further classification results and update the classification.

Once the classification results are available, FPEI system 103 candetermine (324) whether a selection stopping criteria has been met,e.g., whether the selection has been exhausted in the sense thatselecting additional potential defects for review from the furtherpotential defects is not cost effective. Such a case can occur, forexample, when all potential defects had been reviewed, when arepresentative part from the selection has been reviewed and noadditional significant information is expected, enough candidates of aspecific kind have been tested, budget or time limits have been met, orthe like. It will be appreciated that one or more selection stoppingcriteria can be applied.

If no selection stopping criteria has been met, then execution returns(336) to selecting (316) yet another part of the potential defects,followed by imaging (320) and classifying (322).

If the selection stopping criteria has been met, FPEI system 103 candetermine (328) whether an examination stopping criteria has been met,e.g., whether further potential defects should be determined for reviewbeyond the ones already determined, or whether examination has beenexhausted. The process may be determined to be exhausted if the timeallotted for examination is over, if the number of review operations hasreached a threshold, if the number of true defects determined hasreached a predetermined threshold, if the number of true defectsdetermined on the previous one or more collections has decreased below apredetermined threshold, or the like. It will be appreciated that one ormore examination stopping criteria can be applied.

If the examination stopping criteria has been met, the process may exit(332). Additionally, results may be output, for example the truedefects, statistics, or the like. It will be appreciated that certainresults, such as true defects, may be output earlier, for exampleimmediately after detection.

If the examination stopping criteria has not been met, execution cancontinue (340) to detecting a new collection of potential defects (312).

Using the classification results, FPEI system 103 and in particularpotential defects determination module 108 can detect (312) potentialdefects from the inspection results, i.e., from the images as stored andthe associated attributes. Rather, any location or area within theimages captured by inspection tool 101 can be determined as a potentialdefect, whether it has been previously detected as a potential defect ornot.

Detecting further potential defects (312) by FPEI system 103 can includeupdating the recipe (322), and using the updated recipe (326) fordetecting the potential defects.

Recipe determination module 140 can determine (322) a new recipe orupdate the existing recipe in order to improve detection of thepotential defects from the images taken by the inspection tool 101,which detection was initially done based on a default recipe determinedupon one or more exemplary object setup wafers. Updating the recipe cancomprise setting improved parameters for the segmentation. For example,polygon boundaries can be changed, segmentation can be re-applied withspecific input, or the recipe can be changed such that areas withsimilar noise levels are segmented together. The default recipe withwhich the object is examined by the inspection tool, can producesegments each having a noise level or noise level range, such thatmultiple noise levels, for example in the order of magnitude ofhundreds, may exist in segments within the images. Updating the recipemay relate to segmenting, i.e., grouping together areas having similarnoise levels, in order to achieve a smaller number, for example a few,noise level ranges. The recipe may further relate to grading parametersand to thresholds associated with each such combined area, above which adefect is considered a true defect.

Typically, when determining potential defects, one or more images aresegmented using the updated recipe, and potential defects are determinedwithin each segment.

Reference is now also made to FIG. 4 , showing an illustrative exampleof defect detection with segmentation, in accordance with certainembodiments of the presently disclosed subject matter. FIG. 4 showsimage 400 in which areas 404 and 408 are detected by prior art solutionsas potential defects. Areas 404 and 408 are graded, i.e. assigned agrade indicating their likelihood to be a true defect. Grading is donerelative to the whole area of image 400, and thus area 404 is assigned agrade, i.e., a probability to be a defect of 23 with a signal to noiseratio of 0.27, and area 408 is assigned a grade of 86 with a signal tonoise ratio of 3.7. Thus, area 408 is more likely to represent a defectthan area 404. It will be appreciated that the background of FIG. 4 isgenerally less uniform than depicted, and comprises objects and amultiplicity of gray levels. The uniform background is therefore forexplanatory purposes only.

In accordance with some exemplary embodiments of the disclosure,re-detecting the potential defects (326) may comprise segmenting one ormore inspection images stored in image storage 112, or segmenting themin a different manner if the images have previously been segmented, suchthat the locations identified as potential defects are those locationswhich are more prominent within their respective segments. In someexamples, the images may be segmented in accordance with the noiselevels, such that the noise levels within each segment are relativelyuniform or within a small range. A threshold may be associated with eachsegment, such that locations or areas within the segment exceeding thethreshold are prominent and can be identified as potential defects.Pixel groups may be compared against other pixel groups within the samesegment. If the groups are similar, the probability of these groups torepresent a defect is decreased, as defects can be more random.

Image 402 of FIG. 4 shows areas 404 and 408 as above, wherein each isdetected in a separate segment, such as segment 412 comprising area 404and segment 416 comprising area 408. For example, a die (also referredto as a chip) may comprise areas arranged as one or more arrays, andother areas which are more chaotic. In some optical configurations ofinspection tool 101, the arrays may reflect the light in a differentmanner than the other areas. By considering areas of relatively uniformnature, the number of gray levels within each area may be smaller thanwhen array areas and non-array areas are comprised in one segment. Forexample, one area may comprise only 2-10 gray levels (out of the 256possible gray levels). Thus, a gray level threshold differentiatingbetween a potential defect and a normal location may be set such thatdefects which are depicted in gray levels other than these 2-10 graylevels are prominent and can be easily detected within such a segment.Other areas may be noisier and can comprise a larger number of graylevels, thus the threshold can be set to a different threshold thanquiet areas. Setting the threshold in accordance with the noise levelwithin each area provides for improved Signal to Noise Ratio (SNR) andthus better detection. Thus, in the example of FIG. 4 , segments 412 and416 are assigned different thresholds, which can make area 404 moreprominent within segment 412 than area 408 within segment 416.

It will be appreciated that segmentation is more effective whenperformed using knowledge of some locations previously identified aspotential defects and verified to be true defects or proven to be falsealarms by the review tool. For example, using such classificationinformation, it is known whether an area of a certain gray level withina larger area is part of the structure of the larger area, or is adefect, and the threshold for detecting defects within this larger areacan be set accordingly. Thus, since by setting a specifically adaptedthreshold for each area, the potential defects can be more prominent andthus more easily detected. Unlike prior art solutions, in which thepotential defects are determined based on inspection tool results, andfurther potential defects cannot be detected, the iterative mannerdisclosed above provides for making the process more efficient anddetecting more true defects.

Even further, in prior art solutions, the same default setup andsegmentation is used for all objects of a specific type. The disclosedsolution, however, provides for adaptive setup of detection parameters

including segmentation, in which the recipe used during defect detectionis specifically adapted to the object and is also updated in accordancewith newly acquired data to provide efficient defect detection.

It will be appreciated that segmentation is not necessarily associatedwith a geometric division of the object images, but other divisions canbe used as well. For example, similar structures that are geometricallysimilar can be grouped together and be assigned the same or a similarthreshold. In another example, it may be learned from the review imagingthat many false alarms are located on specific areas or on specificfeatures of the design of the object. Thus, these areas can be assignedan appropriate threshold, such that fewer defects will be detectedtherein. Additionally or alternatively, potential defects detected fromspecific areas may be selected for review (320) with lower priority. Inanother example, if the stored images comprise images from amultiplicity of scans, and more potential defects from one scan areproven to be true defects than from another scan, the second scan can beassigned a higher threshold or can even be ignored, such that more truedefects will be detected.

Once the various areas are assigned thresholds, each location having avalue that exceeds the threshold, for example becomes distinguishablefrom its environment, can be assigned a grade.

The segmentation and/or grading detailed below may utilize additionalattributes which may be associated with each location, such as but notlimited to any one or more of the following attributes: whether thelocation has a black or white background, whether the defect wasdetected on an image taken by an inspection tool with particular opticsettings, the noise level in the environment of the location, or thelike.

Re-detecting the potential defects may comprise grading, which canrelate to assigning a probability to each location or each element whichis prominent within its segment, e.g., exceeds the threshold, and canthus be a potential true defect. It will be appreciated that an elementcan refer to a location indicated as a pixel, as a group of connectedpixels, as a feature, as a geometric shape, or the like. Grading maytake into account how much the gray level of a specific location differsfrom the gray level of the locations of the respective segment; the graylevel relative to neighboring locations within the segment, or the like.Grading can also take into account one or more of the attributesdetailed above. Thus, the results of the grading are intensivelyaffected by the segmentation and the threshold assigned to each segment,which in turn depend on the true/false information available for defectspreviously imaged by review tool 102. Additionally or alternatively,grading may comprise applying functions, for example convoluting thegray level values of the image with a function that gives a positiveweight to the locations associated with potential true defects, and anegative weight to locations associated with false alarms, for exampleas follows: |f⊗Image|−|g⊗Image|, wherein f and g can be matched filters,such that the f filter matches a distinct shape associated with a truedefect increases the grade of true defect, and the g filter matches adistinct shape associated with a false alarm.

Thus, such function or similar ones may provide for increasedprobability of true defects and decreased probability of false alarms.Thus, area 404 graded within segment 412 has a better SNR and isassigned a higher grade than area 408 graded within segment 416, whichreverses their grading in respect to the whole image as shown on image400.

In some embodiments, during grading a probability is assigned to thepotential defects in a multiplicity of segments, such that all potentialdefects are on substantially the same scale and their grades can becompared, rather than the potential defects of each segment having theirown scale.

It will be appreciated that initial grading may be performed fordetermining the initial potential defects, before any potential defectwas imaged by review tool 102. However, the initial grading is based onthe sample object and the default recipe, and does not rely oninformation regarding whether any potential defect is a true defect or afalse alarm, and is thus significantly deficient.

Determination of the further potential defects can then be performed inaccordance with the grading results and/or in accordance withthresholds. If, as described above, all potential defects are adjustedto be of substantially the same scale, the potential defects from allsegments can be collected and sorted to form a unified list.

Selecting (316) part of the potential defects for imaging by review tool102 can be performed in a multiplicity of ways. If the potential defectsfrom all segments have been sorted into a unified list, the toppredetermined number of potential defects can be selected, regardless ofthe segment they belong to. Alternatively, the same number of potentialdefects can be selected from each segment, wherein the highest gradedpotential defects are selected within each segment. In yet anotherembodiment, the number of potential defects selected from each segmentis proportional to its area. It will be appreciated that furtherselection schemes may be designed without deviating from the disclosure.

It is to be understood that the invention is not limited in itsapplication to the details set forth in the description contained hereinor illustrated in the drawings. The invention is capable of otherembodiments and of being practiced and carried out in various ways.Hence, it is to be understood that the phraseology and terminologyemployed herein are for the purpose of description and should not beregarded as limiting. As such, those skilled in the art will appreciatethat the conception upon which this disclosure is based may readily beutilized as a basis for designing other structures, methods, and systemsfor carrying out the several purposes of the presently disclosed subjectmatter.

It will also be understood that the system according to the inventionmay be, at least partly, implemented on a suitably programmed computer.Likewise, the invention contemplates a computer program being readableby a computer for executing the method of the invention. The inventionfurther contemplates a non-transitory computer-readable memory tangiblyembodying a program of instructions executable by the computer forexecuting the method of the invention.

Those skilled in the art will readily appreciate that variousmodifications and changes can be applied to the embodiments of theinvention as hereinbefore described without departing from its scope,defined in and by the appended claims.

What is claimed is:
 1. A system comprising: a memory; and a processor,operatively coupled with the memory, to: receive inspection data thatcorresponds to a plurality of potential defects of an object; identify afirst set of locations of first potential defects of the plurality ofpotential defects; image the first set of locations of the firstpotential defects with a review tool to obtain a first set of reviewimages; classify the first potential defects based on the first set ofreview images to obtain first classification results of the firstpotential defects; determine an instruction for the review tool based onthe first classification results, the instruction being associated withdetecting potential defects; and identify, using the instruction, asecond set of locations of second potential defects of the plurality ofpotential defects to be imaged with the review tool.
 2. The system ofclaim 1, wherein the object is a semiconductor wafer.
 3. The system ofclaim 1, wherein the processor is further to: determine whether anexamination stopping criterion has been satisfied, wherein theexamination stopping criterion corresponds to a number of reviewoperations, and wherein the examination stopping criterion is notsatisfied responsive to the number of review operations not satisfying athreshold number of review operations.
 4. The system of claim 3, whereinthe processor is further to: wherein the examination stopping criterioncorresponds to a number of defects that have been identified, andwherein the examination stopping criterion is not satisfied responsiveto the number of defects not satisfying a threshold number of defects.5. The system of claim 1, wherein the second set of locations isdifferent from the first set of locations.
 6. The system of claim 1,wherein the first classification results are associated with whether arespective potential defect is a correctly-identified defect or is notthe correctly-identified defect.
 7. The system of claim 1, wherein thefirst classification results are associated with an identification of atype of defect of a respective potential defect.
 8. A method comprising:receiving inspection data that corresponds to a plurality of potentialdefects of an object; identifying a first set of locations of firstpotential defects of the plurality of potential defects; imaging thefirst set of locations of the first potential defects with a review toolto obtain a first set of review images; classifying the first potentialdefects based on the first set of review images to obtain firstclassification results of the first potential defects; determining aninstruction for the review tool based on the first classificationresults, the instruction being associated with detecting potentialdefects; and identifying, using the instruction, a second set oflocations of second potential defects of the plurality of potentialdefects to be imaged with the review tool.
 9. The method of claim 8,wherein the object is a semiconductor wafer.
 10. The method of claim 8,further comprising: determining whether an examination stoppingcriterion has been satisfied, wherein the examination stopping criterioncorresponds to a number of review operations, and wherein theexamination stopping criterion is not satisfied responsive to the numberof review operations not satisfying a threshold number of reviewoperations.
 11. The method of claim 8, further comprising: determiningwhether an examination stopping criterion has been satisfied, whereinthe examination stopping criterion corresponds to a number of defectsthat have been identified, and wherein the examination stoppingcriterion is not satisfied responsive to the number of defects notsatisfying a threshold number of defects.
 12. The method of claim 8,wherein the second set of locations is different from the first set oflocations.
 13. The method of claim 8, wherein the first classificationresults are associated with whether a respective potential defect is acorrectly-identified defect or is not the correctly-identified defect.14. The method of claim 8, wherein the first classification results areassociated with an identification of a type of defect of a respectivepotential defect.
 15. A non-transitory computer readable mediumcomprising instructions, which when executed by a processor, cause theprocessor to perform operations comprising: receiving inspection datathat corresponds to a plurality of potential defects of an object;identifying a first set of locations of first potential defects of theplurality of potential defects; imaging the first set of locations ofthe first potential defects with a review tool to obtain a first set ofreview images; classifying the first potential defects based on thefirst set of review images to obtain first classification results of thefirst potential defects; determining an instruction for the review toolbased on the first classification results, the instruction beingassociated with detecting potential defects; and identifying, using theinstruction, a second set of locations of second potential defects ofthe plurality of potential defects to be imaged with the review tool.16. The non-transitory computer readable medium of claim 15, wherein theobject is a semiconductor wafer.
 17. The non-transitory computerreadable medium of claim 15, wherein the processor is to perform furtheroperations comprising: determining whether an examination stoppingcriterion has been satisfied, wherein the examination stopping criterioncorresponds to a number of review operations, and wherein theexamination stopping criterion is not satisfied responsive to the numberof review operations not satisfying a threshold number of reviewoperations.
 18. The non-transitory computer readable medium of claim 15,wherein the processor is to perform further operations comprising:determining whether an examination stopping criterion has beensatisfied, wherein the examination stopping criterion corresponds to anumber of defects that have been identified, and wherein the examinationstopping criterion is not satisfied responsive to the number of defectsnot satisfying a threshold number of defects.
 19. The non-transitorycomputer readable medium of claim 15, wherein the second set oflocations is different from the first set of locations.
 20. Thenon-transitory computer readable medium of claim 15, wherein the firstclassification results are associated with whether a respectivepotential defect is a correctly-identified defect or is not thecorrectly-identified defect.